چکیده:
In this paper we give an overview of newest intelligent optimization methods in electrical engineering., In an optimization problem, the types of mathematical relationships between the objective function and constraints and the decision variables determine how hard it is to solve, the solution methods or algorithms that can be used for optimization, and the confidence we can have that the solution is truly optimal. In this work, most of the algorithms and applications about Intelligent optimization have been collected and described. It is explained how to solve each optimization problem, step by step with the simplest possible form.
خلاصه ماشینی:
, In an optimization problem, the types of mathematical relationships between the objective function and constraints and the decision variables determine how hard it is to solve, the solution methods or algorithms that can be used for optimization, and the confidence we can have that the solution is truly optimal.
Similar to other evolutionary algorithms,ICA begins with an initial population and is separated into two types: the colonies and the imperialists (the ones with the best objective function values).
By using the adaptive immune response, this algorithm can be used to search for the best solution of an optimization problem.
Based on ]11-12[,The iterative approach of SFLA can be described by the following steps: Step1: Initialize parameters: -Population size -number of memeplexes -number of iterations within each memeplex Step2: Generation of initial population(P) randomly and evaluating the fitness of each frog Step3: Sorting population in descending order in term of fitness value Step4: Distribution of frogs into (m) memeplexes Step5: Local search Iterative updating the worst frog of each memeplex Step6: Shuffle the memeplexes Step7: Check termination condition.
Step6:Termination Condition: The whole process continues until the maximum number of iterations has been reached, and we hope that the plant with the best fitness is the closest one to the optimal solution.
In this step, we initialize the parameters of algorithm, generate and also evaluate the initial population, and then determine the best solution xbest in the population.